Towards High-Dimensional Computational Steering of Precomputed Simulation Data using Sparse Grids

With the ever-increasing complexity, accuracy, dimensionality, and size of simulations, a step in the direction of data-intensive scientific discovery becomes necessary. Parameter-dependent simulations are an example of such a data-intensive tasks: The researcher, who is interested in the dependency of the simulation's result on a set of input parameters, changes essential parameters and wants to immediately see the effect of the changes in a visual environment. In this scenario, an interactive exploration is not possible due to the long execution time needed by even a single simulation corresponding to one parameter combination and the overall large number of parameter combinations which could be of interest. In this paper, we present a method for computational steering with pre-computed data as a particular form of visual scientific exploration. We consider a parametrized simulation as a multi-variate function in several parameters. Using the technique of sparse grids, this makes it possible to sample and compress potentially high-dimensional parameter spaces and to efficiently deliver a combination of simulated and precomputed data to the steering process, thus enabling the user to interactively explore high-dimensional simulation results.